Transfer Learning for Estimating Causal Effects using Neural Networks
This work addresses the challenge of efficient causal effect estimation for researchers and practitioners, offering a significant improvement over current methods.
The paper tackled the problem of estimating heterogeneous treatment effects by combining transfer learning for neural networks with causal inference, achieving an order of magnitude better performance than existing benchmarks while using a fraction of the data.
We develop new algorithms for estimating heterogeneous treatment effects, combining recent developments in transfer learning for neural networks with insights from the causal inference literature. By taking advantage of transfer learning, we are able to efficiently use different data sources that are related to the same underlying causal mechanisms. We compare our algorithms with those in the extant literature using extensive simulation studies based on large-scale voter persuasion experiments and the MNIST database. Our methods can perform an order of magnitude better than existing benchmarks while using a fraction of the data.